Add profiling for on_load_checkpoint/on_save_checkpoint callback and LM hooks

CHANGELOG.md

@@ -143,6 +143,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for pluggable Accelerators ([#12030](https://github.com/PyTorchLightning/pytorch-lightning/pull/12030))
 
 
+- Added profiling for `on_load_checkpoint`/`on_save_checkpoint` callback and LightningModule hooks ([#12149](https://github.com/PyTorchLightning/pytorch-lightning/pull/12149))
+
+
 - Added `LayerSync` and `NativeSyncBatchNorm` plugins ([#11754](https://github.com/PyTorchLightning/pytorch-lightning/pull/11754))
 
 

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -167,7 +167,7 @@ def restore_model(self) -> None:
         model = self.trainer.lightning_module
 
         # hook: give user access to checkpoint if needed.
-        model.on_load_checkpoint(self._loaded_checkpoint)
+        self.trainer._call_lightning_module_hook("on_load_checkpoint", self._loaded_checkpoint)
 
         # TODO: remove this in v1.8.
         # call hpc specific hook
@@ -393,7 +393,7 @@ def dump_checkpoint(self, weights_only: bool = False) -> dict:
             # support for returning state in on_save_checkpoint
             # will be removed in v1.8
             self.trainer._call_callbacks_on_save_checkpoint(checkpoint)
-        model.on_save_checkpoint(checkpoint)
+        self.trainer._call_lightning_module_hook("on_save_checkpoint", checkpoint)
         if datamodule is not None:
             datamodule.on_save_checkpoint(checkpoint)
 

pytorch_lightning/trainer/trainer.py

@@ -1677,19 +1677,33 @@ def _call_callbacks_on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None
         Will be removed in v1.8: If state is returned, we insert the callback state into
         ``checkpoint["callbacks"][Callback.state_key]``. It overrides ``state_dict`` if already present.
         """
+        pl_module = self.lightning_module
+        if pl_module:
+            prev_fx_name = pl_module._current_fx_name
+            pl_module._current_fx_name = "on_save_checkpoint"
+
         for callback in self.callbacks:
-            # TODO: Add profiling for on_save_checkpoint hook
-            state = callback.on_save_checkpoint(self, self.lightning_module, checkpoint)
+            with self.profiler.profile(f"[Callback]{callback.state_key}.on_save_checkpoint"):
+                state = callback.on_save_checkpoint(self, self.lightning_module, checkpoint)
             if state:
                 # TODO: Add deprecation warning if state is returned (see reference PR #11887)
                 checkpoint["callbacks"][callback.state_key] = state
 
+        if pl_module:
+            # restore current_fx when nested context
+            pl_module._current_fx_name = prev_fx_name
+
     def _call_callbacks_on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
         """Called when loading a model checkpoint.
 
         Calls every callback's `on_load_checkpoint` hook. We have a dedicated function for this rather than using
         `_call_callback_hooks` because we have special logic for getting callback_states.
         """
+        pl_module = self.lightning_module
+        if pl_module:
+            prev_fx_name = pl_module._current_fx_name
+            pl_module._current_fx_name = "on_load_checkpoint"
+
         callback_states: Dict[Union[Type, str], Dict] = checkpoint.get("callbacks")
 
         if callback_states is None:
@@ -1709,8 +1723,12 @@ def _call_callbacks_on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None
             state = callback_states.get(callback.state_key, callback_states.get(callback._legacy_state_key))
             if state:
                 state = deepcopy(state)
-                # TODO: Add profiling for on_load_checkpoint hook
-                callback.on_load_checkpoint(self, self.lightning_module, state)
+                with self.profiler.profile(f"[Callback]{callback.state_key}.on_load_checkpoint"):
+                    callback.on_load_checkpoint(self, self.lightning_module, state)
+
+        if pl_module:
+            # restore current_fx when nested context
+            pl_module._current_fx_name = prev_fx_name
 
     def _call_callbacks_load_state_dict(self, checkpoint: Dict[str, Any]) -> None:
         """Called when loading a model checkpoint, calls every callback's `load_state_dict`."""

tests/trainer/logging_/test_logger_connector.py

@@ -243,6 +243,9 @@ def test_fx_validator_integration(tmpdir):
         "on_validation_model_eval": "You can't",
         "on_validation_model_train": "You can't",
         "lr_scheduler_step": "You can't",
+        "on_save_checkpoint": "You can't",
+        "on_load_checkpoint": "You can't",
+        "on_exception": "You can't",
         "summarize": "not managed by the `Trainer",
     }
     model = HookedModel(not_supported)